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All Journal Journal of Business and Information System Fairness
Cindi Ferdiani
Universitas Islam Nahdlatul Ulama Jepara
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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Digital financial literacy, social capability, dan financial resilience di era digital Cindi Ferdiani; Anna Widiastuti; Hadi Ismanto
Journal of Business and Information Systems (e-ISSN: 2685-2543) Vol. 8 No. 1 (2026): Journal of Business and Information Systems
Publisher : Department of Accounting, Faculty of Business, Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jbis.v8i1.351

Abstract

This study aims to examine the effects of digital financial literacy and social capability on household financial resilience, with financial capability and fintech usage as mediators. A quantitative approach was employed using a survey of households in Indonesia. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that digital financial literacy has a significant effect on financial resilience through financial capability and fintech usage. Furthermore, financial capability serves as a key mediator in the relationships between both digital financial literacy and social capability with financial resilience. Meanwhile, fintech usage mediates the effect of digital financial literacy on financial resilience, but does not mediate the relationship between social capability and financial resilience. These findings suggest that the adoption of financial technology is driven more by individual literacy and cognitive capacity than by social factors. This study highlights the importance of strengthening digital financial literacy and financial capability as strategic pathways to enhance household financial resilience in the digital era
Literature Analysis on Financial Distress and Bankruptcy Prediction Rias Untian Hanun; Cindi Ferdiani
Fairness Vol. 1 No. 1 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-fr.2025.1(1)-02

Abstract

Objective: This study aims to analyze financial distress prediction models that have been used in various academic studies, evaluate the accuracy of models in various industry sectors, and identify factors that affect the accuracy of predicting corporate bankruptcy. Research Design & Methods: This research uses a systematic literature review (SLR) to evaluate the effectiveness of financial distress prediction models based on studies from reputable journals in the range 2015-2024. Findings: The results show that the Altman Z-Score and Ohlson O-Score have the highest accuracy rate (90.91%), making them the most widely used models in the manufacturing and banking industries. The Zmijewski Model has an accuracy of 86.36%, more suitable for high asset-based sectors such as mining and transportation. The Springate Model, with an accuracy rate of 63.64% - 73.48%, is simpler but less accurate than the other models, especially in the service-based and financial sectors The research also found that the logit regression-based model (Ohlson O-Score) is superior in considering external factors, such as company size and macroeconomic conditions, compared to other models that focus more on financial ratios. Implications & Recommendations: Any financial distress prediction model has advantages and limitations that depend on industry characteristics. Therefore, their selection should consider the financial structure, industry sector, and external factors such as regulation and economic dynamics. The integration of traditional models with machine learning and artificial intelligence (AI) is recommended to improve the accuracy and effectiveness of early detection. Contribution & Value Added: This research provides insights for academics, practitioners, and regulators on the accuracy of financial distress prediction models and emphasizes the need for an adaptive approach that integrates financial and non-financial factors to improve business resilience
Co-Authors Anna Widiastuti Hadi Ismanto Rias Untian Hanun